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Transform data management with DataOps and a data fabric

Chapter 02
5 min read

Every organization is looking for ways to unlock its data to innovate and stay competitive, but most face significant challenges with collecting, storing and analyzing data for AI. Data environments have grown increasingly complex, with disparate data types and sources dispersed across the data landscape.


75% of larger companies report drawing from more than 20 different data sources to inform their AI, business intelligence and analytics systems.1

To fuel AI with the most relevant, trustworthy information, business data will need to be discovered, cataloged and transformed, and organizations will need protocols for ensuring data privacy and regulatory compliance. A robust data management strategy and the supporting information architecture can help you unlock business data and discover insights to transform your business.


More than 50% of firms struggle with data integration when it comes to both data science and machine learning platforms and analytics and business intelligence platforms.2

Establishing a data foundation built on DataOps principles
DataOps is a methodology that enables businesses to strategically design their information architecture and use AI-powered automation to derive maximum value from their data.

DataOps is the orchestration of people, processes and technology to deliver trusted data to digital citizens fast.

DataOps principles can help your organization achieve:

  • Data cataloging to provide definitions that facilitate self-service management
  • Data preparation to transform raw data into consumable information
  • Data quality assessment to ensure the best business-ready data
  • Data integration to meet data access and delivery needs
  • Data privacy and compliance definition and enforcement

A DataOps approach helps drive agility, speed and new data initiatives at scale, empowering your business to use AI while ensuring proper governance and security controls.

Modernize your information architecture with a data fabric
A strong foundation for AI success requires more than just a methodology or set of principles; organizations also need to modernize their information architecture technology. That is, you need an architecture designed for AI — one that will help you optimize and automate data access and availability, deliver high-quality governed data and manage privacy and compliance.

A data fabric is an architecture that dynamically orchestrates disparate sources across a hybrid and multicloud landscape to provide business-ready data for AI.

Data fabric is the technological connective tissue between data endpoints that enables the full range of data management capabilities: discovery, integration, governance, curation and orchestration. It equips data citizens with access to the right data at the right time.

A data fabric employs emerging technologies such as machine learning (ML), data virtualization, a semantic layer, metadata management and automated data cataloging to break down the boundaries separating applications, data, clouds and people.

A data fabric offers 3 key benefits:

  • Self-service data consumption and collaboration
  • Automated governance, data protection and compliance
  • Data integration across a hybrid and multicloud data landscape

By implementing an information architecture designed for AI — and underpinned by DataOps principles — your business can eliminate data silos, govern the data and AI lifecycle, and run anywhere with agility. Ultimately, the right architecture and principles can help you operationalize AI with trust and transparency.

1 Global AI Adoption Index 2021 (PDF, 5.9 MB), IBM and Morning Consult, 2021.